Search: “jerry-liu--x”

sorry to every other sf tech week host in advance, but we may have gotten carried away planning our event... we're about to host the biggest and most lavish black-tie gala sf tech has ever seen. live

This is a fantastic blog post from our research team on the importance of calibrated confidence scores. Here it's in the context of document extraction, it's also extremely important for more general

we've built the world's most advanced engine for document extraction over complex documents the video below shows an overview of LlamaExtract Agentic Plus in action. long tables, giant forms, calibrat

Excited for next Tuesday, Sept 29, and our monthly @daytonaio AI Builders Demo Night in SF! We’re hosting at @WorkOS SF Office in partnership with @llama_index. Join us for an evening of great demos,

holy fuck it's christmas Please welcome GPT-6 Sol and GPT-6 Luna to the GPT-6 universe. GPT-6 Sol and Luna build on the advances behind GPT-6 Astra, bringing much of its strengths into faster and more

Opus 5.5 is the best frontier model for parsing tables in PDFs. We ran it through ParseBench, and it scored 93.9%, a 7%+ increase over Opus 5, and beating Fable/Gemini/Astra. It's comparable to LlamaP

The fastest PDF-to-Markdown parser just got faster. ⚡️ With LiteParse v2.14.6, text-based PDFs parse about 25% faster. Across realistic documents, LiteParse processed pages at 2.8ms/page and 1.5× fast

Document classification and splitting idea using @typesafeai Jev based on LiteParse, and LlamaParse. Classify: categorize probabilities, and review flags. Measure: separate OCR and decision timing, pr

Grounded Confidence is here for Extract! 🦙 When your agents and workflows depend on extracted data, you need to know how accurate that data is. We’ve added confidence scores to give you a better read

with jev, everyone is understanding the importance of calibrated confidence scores for discrete decision making we've taken that approach one-step further and created grounded confidence scores for ge

Doc + Jev => DocJev @llama_index 团队最新开源文档分类与拆分工具 DocJev，给它一个 PDF/DOCX/PPTX 和一份用自然语言写的类别规则，它可以告诉你这份文档属于哪个类别，或者把一份“文件包”按边界拆成若干子文档并给出各自页码范围。 开源地址：https://github.com/jerryjliu/docjev 核心架构：解析与决策分离 1. 解析层（O

Introducing DocJev - a lightning-fast OSS library for document classification and splitting with jev ⚡️ Give a document alongside some natural language category rules. Jev will predict the document ca

Jeff has been added to JevBench! Ranked at #9 currently 🚀 https://github.com/logan-markewich/jeff Jeff is a drop-in replacement for jev based on GliFormer from @knowledgator. The only models ranking h

LlamaIndex 发布了一个面向 AI Agent 的文档解析评测基准「ParseBench」 @llama_index 100 种解析方法参与评测（vlm - 67、专用解析器 - 33），2000 页真实企业文档（人工两轮校验），169011 条确定性规则，完全不用 LLM 当裁判，可复现。评测五个维度取无加权平均：表格结构、图表抽取、内容忠实度、格式语义、视觉定位。 https://ww

Recently, Datalab released OmniExtractBench, a benchmark that combines several benchmarks (our own ExtractBench, LongExtractBench, other vendors) to measure structured document quality. The benchmark

what if we...increased the air gap OpenAI's Noam Brown says air-gapping the computers may not stop a misaligned AI, because two air-gapped machines can still talk by running a CPU hot and reading the

with jev, it's nice to see builder energy back on X, reminiscent of the early days of building with LLMs (back when gpt_index/llama_index/langchain first started) before jev, most of the 2026 AI demo

It was great to be at Connected Stack last week with founders and builders working on what’s next in enterprise AI. @jerryjliu0 joined the Founder Flash Talks to talk about a problem every enterprise

The difference with using VLMs for OCR vs. other tasks is that the ratio of output/input tokens is quite high - meaning the model can spend a lot of time writing markdown. Speculative decoding offers

au revoir, Stainless Building production-level SDKs is extremely important for any developer product, and also really hard. This is a fun post from our engineering team on why there's value in tooling

Ready to learn how to build document processing pipelines for insurance agents? Join LlamaIndex Solutions Architect Abrar Mahi for a webinar on turning insurance documents into structured data for und

keeping our SDKs up to date can be a pain. every API change brings another round of updates, tests, and releases. and each SDK should still feel like it was written by someone who actually uses the la

i'm flattered by the shoutout, but there's no free lunch for document parsing Cohere parse starts at $1.50 / 1k pages, which is indeed on the cheaper end of doc OCR solutions, but it scores ~50% when

just-in-time OCR is all the rage. most pipelines parse every page before anyone asks a question. for an agent working through an ad-hoc data room, that's slow, expensive, and most of those pages never

“Just-in-Time” OCR The latest agent harnesses (e.g. Cowork, Codex, Grok) do 2 passes of ad-hoc document processing when executing over a user-uploaded ‘data room’ of documents: 1️⃣ A fast and light pa

Jerry Liu suggested that users who don’t want to rely on a “fly brain” approach for reading PDFs could instead use an OCR solution called **llamaparse** hosted at *cloud.llamaindex.ai*. The tweet, pos

agreed, one of the biggest bottlenecks right now is my own ability to internalize the information that these models convey the latest models (astra, fable 5.1) are either way too verbose, or try too h

Astra is one of the best models we've tested at parsing complex documents with tables, at 93.2% on ParseBench. So it seems well suited for ad-hoc analysis over table-heavy documents e.g. in finance. T

confidence scores in LlamaParse just got an upgrade 🦸‍♀️ our new high-effort mode provides granular page-level scores with text explanations to help you intimately understand parsing quality of your d

Last Friday night, our team hosted a lit event at the US Open 🎾 we gathered a group of enterprise and startup AI leaders + founders together to drink, talk about AI and document parsing, and watch ten

let Astra cook 👩‍🍳 let LlamaParse read 🤓 Last week we brought LlamaParse to Claude. Now it’s available in ChatGPT, too 🦙 ChatGPT can already read PDFs. But document work gets harder when the informati

Lightning-fast extraction over any document. ICYMI, we launched Extract Turbo last week, that lets you process documents from 1 page to dozens of pages with lightning-fast speed. Come check it out! Ch

One of the main issues with AI document parsing is that because no solution is 100% accuracy, it's hard to tell if a given page is parsed incorrectly: for instance a misaligned table, missing/hallucin

We've trained new models for checkbox parsing ☑️ This lets you convert checkboxes of any size and shape, filled or unfilled, into structured JSON that's suitable for programmatic or agentic use. Use i

Astra is awesome, but if you're using it specifically to parse and extract a massive volume of documents, you will break the bank (10c+ per page on parsing and extraction!) We've added a native LlamaP

Last week we brought LlamaParse to Claude. Now it’s available in ChatGPT, too 🦙 ChatGPT can already read PDFs. But document work gets harder when the information you need is buried in messy scans, den

Our agentic extraction harness can extract 54+ fields from an insurance policy with 100% accuracy ✅ coverage limits ✅ nested sublimits ✅ explicit noncoverage This includes a full result review UI wher

holy shit We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics. The proof was produced by a group of agents, using an Open

Astra might finally help me automate what I've spent ~hundreds of hours doing since the start of this company: Making live product demos through Loom or Screen Studio Check out the video walkthrough o

correction: the switching costs weren't enough to stop my desire to try out gpt-6 astra i ported over all my skills and conversational context to codex this weekend. planning to use it as my daily dri

We benchmarked GPT-6 Astra on hard document parsing and extraction tasks. It is the best frontier model for one-shotting document extraction over short and medium-sized documents: 📊 97.2% over short d

Does paying 5x more per page actually get you better document extraction? We ran the data to find out. We evaluated 14 frontier systems across 370 enterprise documents, then plotted their accuracy aga

Gemini 3.8 Flash might be maxxed on other benchmarks, but not quite on document parsing. We benchmarked it on ParseBench. It does slightly better on tables + content faithfulness compared to Gemini 3.

Jerry Liu expressed that he is awaiting access to GPT‑6 “Astra” while subscribed to ChatGPT Pro. According to OpenAI’s tweet, GPT‑6 Astra aims to replicate any computer task and deliver results quickl

Unlike other players in the OCR space, we don't block our competitors from accessing our platform. We're also fully committed towards open, reproducible benchmarks for document parsing and extraction

We've massively improved our document parsing capabilities across the board in the past ~3 months. Our LlamaParse cost-effective mode is one of the cheapest OCR solutions out there at <0.4c per page,